{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scatter_hist: create a scatter histogram plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function to quickly produce a scatter histogram plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.plotting import scatter_hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "- https://matplotlib.org/gallery/lines_bars_and_markers/scatter_hist.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Scatter Plot and Histograms from Pandas DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length [cm]</th>\n",
       "      <th>sepal width [cm]</th>\n",
       "      <th>petal length [cm]</th>\n",
       "      <th>petal width [cm]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length [cm]  sepal width [cm]  petal length [cm]  petal width [cm]\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.data import iris_data\n",
    "from mlxtend.plotting import scatter_hist\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "X, y = iris_data()\n",
    "df = pd.DataFrame(X)\n",
    "df.columns = ['sepal length [cm]', 'sepal width [cm]', 'petal length [cm]', 'petal width [cm]']\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import scatter_hist\n",
    "\n",
    "\n",
    "fig = scatter_hist(df[\"sepal length [cm]\"], df[\"sepal width [cm]\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2 - Category Scatter from NumPy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.data import iris_data\n",
    "from mlxtend.plotting import scatter_hist\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "X, y = iris_data()\n",
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = scatter_hist(X[:, 0], X[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## scatter_hist\n",
      "\n",
      "*scatter_hist(x, y, xlabel=None, ylabel=None, figsize=(5, 5))*\n",
      "\n",
      "Scatter plot and individual feature histograms along axes.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `x` : 1D array-like or Pandas Series\n",
      "\n",
      "    X-axis values.\n",
      "\n",
      "\n",
      "- `y` : 1D array-like or Pandas Series\n",
      "\n",
      "    Y-axis values.\n",
      "\n",
      "\n",
      "- `xlabel` : str (default: `None`)\n",
      "\n",
      "    Label for the X-axis values. If `x` is a pandas Series,\n",
      "    and `xlabel` is `None`, the label is inferred automatically.\n",
      "\n",
      "\n",
      "- `ylabel` : str (default: `None`)\n",
      "\n",
      "    Label for the X-axis values. If `y` is a pandas Series,\n",
      "    and `ylabel` is `None`, the label is inferred automatically.\n",
      "\n",
      "\n",
      "- `figsize` : tuple (default: `(5, 5)`)\n",
      "\n",
      "    Matplotlib figure size.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `plot` : Matplotlib Figure object\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/scatter_hist.md', 'r') as f:\n",
    "    s = f.read()\n",
    "print(s)"
   ]
  }
 ],
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